Dynamic Systems Estimation - Multivariate Time Series Package
Functions for time series modeling, including multi-variate state-space and ARMA (VAR, ARIMA, ARIMAX) models.
A Brief User's Guide is distributed with dse as a vignette. The package implements an R/S style object approach to time series modeling. This means that different model and data representations can be implemented with fairly simple extensions to the package.
The package includes methods for simulating, estimating, and converting among different model representations. These are mainly in dse. Package EvalEst has methods for studying estimation techniques and for examining the forecasting properties of models. There are also functions for forecasting and for evaluating the performance of forecasting models, as well as functions for evaluating model estimation techniques.
|Depends:||R, setRNG, tframe|
|License:||free, see LICENSE file for details.|
The main objects are:
time series input and output data structure
a DSE model structure
model, data and some estimation information
The main general methods are:
create, extract a DSE data structure
create, extract a DSE model structure
simulate a model to produce artifical data
convert to a state-space model
convert to an ARMA model
construct an ARMA model
construct a state-space model
evaluate a model with data
calculate the smoothed state estimate
The main estimation methods are:
estimate an ARMA model with least squares
estimate an ARMA model with ar
calculate a state-space model from an estimated VAR model
a (usually) good “black-box” estimated model
estimate a model using maximum likelihood
The main diagnositic methods are:
calculate several information tests for a model
calculate the McMillanDegree of a model
calculate the stability of a model
calculate the roots of a model
The methods for producing and evaluating forecasts are:
evaluate a model with data (and simple forecasts)
calculate forecasts starting at different periods
calculate forecasts at different horizons
calculate the covariance of forecasts
The methods for evaluating estimation methods are:
evaluate estimation methods
The functions described in the Brief User's Guide and examples in the help pages should work fairly reliably (since they are tested regularly), however, the code is distributed on an “as-is” basis. This is a compromise which allows me to make the software available with minimum effort. This software is not a commercial product. It is the by-product of ongoing research. Error reports, constructive suggestions, and comments are welcomed.
Anderson, B. D. O. and Moore, J. B. (1979) Optimal Filtering. Prentice-Hall.
Gilbert, P. D. (1993) State space and ARMA models: An overview of the equivalence. Working paper 93-4, Bank of Canada. Available at http://www.bankofcanada.ca/1993/03/publications/research/working-paper-199/
Gilbert, P. D. (1995) Combining VAR Estimation and State Space Model Reduction for Simple Good Predictions. J. of Forecasting: Special Issue on VAR Modelling. 14:229–250.
Gilbert, P.D. (2000) A note on the computation of time series model roots. Applied Economics Letters, 7, 423–424
Jazwinski, A. H. (1970) Stochastic Processes and Filtering Theory. Academic Press.